%0 Journal Article %1 citeulike:154 %A Newman, M. E. J. %A Girvan, M. %D 2004 %J Physical Review E %K aaa community %R 10.1103/PhysRevE.69.026113 %T Finding and evaluating community structure in networks %U http://arxiv.org/abs/cond-mat/0308217 %V 69 %X We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.

@article{citeulike:154, abstract = {We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.}, added-at = {2006-06-16T10:34:37.000+0200}, author = {Newman, M. E. J. and Girvan, M.}, biburl = {https://www.bibsonomy.org/bibtex/2c565fe3c95b04c01e286ebbde3366bdd/ldietz}, citeulike-article-id = {154}, comment = {Girvan Newman Algorithm Girvan-Newman Algorithm GN Algorithm}, doi = {10.1103/PhysRevE.69.026113}, eprint = {cond-mat/0308217}, interhash = {b9145040e35ccb4d2a0ce18105e64ff4}, intrahash = {c565fe3c95b04c01e286ebbde3366bdd}, journal = {Physical Review E}, keywords = {aaa community}, month = {February}, priority = {2}, timestamp = {2006-06-16T10:34:37.000+0200}, title = {Finding and evaluating community structure in networks}, url = {http://arxiv.org/abs/cond-mat/0308217}, volume = 69, year = 2004 }